The key to understanding losses in quantitative trading has finally become clear. Most retail quants run backtests using gaming graphics cards, and this is the core issue—insufficient computational precision, coupled with inherent data biases. Seemingly tiny errors can accumulate layer by layer, ultimately causing expected returns to slip directly into losses.

Truly stable profitable quantitative models need to be built on high-precision calculations. The industry uses AI supercomputing clusters; a single specialized chip costs hundreds of thousands, renting a complete supercomputing server can cost tens of thousands per month, and a full self-built system can run into millions. This is why quantitative trading is essentially a game for institutional players—ordinary retail investors simply cannot afford such computational power costs.

From another perspective, this also explains why some quantitative funds can sustain profits, while most individual strategies frequently incur losses. A slight difference in precision can result in a gap of hundreds of thousands or even more.